Keynote presentation on policy approaches to socio-technical causes of algorithmic bias at the Bias in Information, Algorithms and Systems workshop at the iConference on 25 March 2018.

1. Policy approaches to socio-technical
causes of bias in algorithmic systems
– what role can ethical standards play?
ANSGAR KOENE,
HORIZON DIGITAL ECONOMY RESEARCH INSTITUTE, UNIVERSITY OF NOTTIN GHAM
25TH MARCH 2018
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2. Data Driven Society
Data driven service optimization Evidence based policy making
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3. Algorithms in the news
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4. Algorithmic Discrimination
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5. Unequal Representation and Gender Stereotypes
in Image Search Results for Occupations
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6. Confirmation Bias: Predictive policing
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7. Complex individuals reduced to
simplistic binary stereotypes
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8. Case study: Recidivism risk prediction
COMPAS recidivism prediction tool
◦ Built by a commercial company, Northpointe, Inc.
Estimates likelihood of criminals re-offending in future
◦ Inputs: Based on a long questionnaire
◦ Outputs: Used across US by judges and parole officers
Are COMPAS’ estimates fair to salient social groups?
Machine Bias: There’s software used across the
country to predict future criminals. Propublica
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9. Is COMPAS fair to all groups?
Northpointe: In each estimated risk level, false discovery rates for blacks & whites are similar
So YES!
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10. Is COMPAS fair to all groups?
ProPublica: False positive & false negative rates are considerably worse for blacks than whites
So NO!
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11. Who is right about COMPAS?
Both! Depends on how you measure fairness!
How many fairness measures can one define?
◦ How many different error rate measures can one define?
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12. But, aren’t the measures similar?
NO! They present inherent trade-offs!
When base recidivism rates for blacks & whites differ, no
non-trivial solution to achieve similar FPR, FNR, FDR, FOR!
No non-trivial solution can be simultaneously fair
according to both ProPublica & Northpointe analyses!
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13. Limited resources assignment problem:
Choose your favourite character to play
Each character can be played by only one player
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14. Satisfaction Distribution
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15. The algorithm decision principles
A1: minimise total disparity
while guaranteeing at least 70%
of maximum overall satisfaction
A2: maximise the minimum
individual satisfaction while
guaranteeing at least 70% of
maximum overall satisfaction
A3: maximise overall
satisfaction
A4: maximise the minimum
individual satisfaction
A5: minimise total disparity
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16. Algorithm preference ratings from a
previous trial with students
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17. Fairness is fundamentally a societally defined construct (e.g. equality of outcomes vs
equality of treatment)
◦ Cultural differences between nations/jurisdictions
◦ Cultural changes in time
“Code is Law”: Algorithms, like laws, both operationalize and entrench spatio-temporal
values
Algorithms, like the law, must be:
◦ transparent
◦ adaptable to change (by a balanced process)
The ‘messy’ problem of fair algorithms
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18. UnBias: Emancipating Users Against Algorithmic
Biases for a Trusted Digital Economy
Standards and policy
Stakeholder workshops
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Youth Juries

19. Parliamentary inquiries
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20. National Initiatives
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21. EU response (in addition to GDPR)
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EU Parliament Science and Technology Options Assessment
(STOA) panel request for study on “Algorithmic Opportunities
and Accountability”

22. Industry response
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23. ACM Principles on Algorithmic
Transparency and Accountability
Awareness
Access and Redress
Accountability
Explanation
Data Provenance
Auditability
Validation and Testing
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25. Ethically Aligned Design
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https://ethicsinaction.ieee.org/

26. IEEE-SA Standards Projects• IEEE P7000: Model Process for Addressing Ethical Concerns During System Design
• IEEE P7001: Transparency of Autonomous Systems
• IEEE P7002: Data Privacy Process
• IEEE P7003: Algorithmic Bias Considerations
• IEEE P7004: Standard on Child and Student Data Governance
• IEEE P7005: Standard on Employer Data Governance
• IEEE P7006: Standard on Personal Data AI Agent Working Group
• IEEE P7007: Ontological Standard for Ethically Driven Robotics and Automation
Systems
• IEEE P7008: Standard for Ethically Driven Nudging for Robotic, Intelligent and
Autonomous Systems
• IEEE P7009: Standard for Fail-Safe Design of Autonomous and Semi-Autonomous
Systems
• IEEE P7010: Wellbeing Metrics Standard for Ethical Artificial Intelligence and
Autonomous Systems
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Open invitation to join the P7003 working group
http://sites.ieee.org/sagroups-7003/

28. P7003 foundational sections
Taxonomy of Algorithmic Bias
Person categorization and identifying affected population groups
Legal frameworks related to Bias
Psychology of Bias
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29. P7003 algorithm development sections
Algorithmic system design stages
Assurance of representativeness fo testing/training data
Evaluation of system outcomes
Evaluation of algorithmic processing
Assessment of resilience against external manipulation to Bias
Documentation of criteria, scope and justifications of choices
Use Cases
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30. Related standards activities
British Standards Institute (BSI) – BS 8611 Ethics design and application of robots
ISO/IEC JTC1 SC42
◦ Artificial Intelligence Concepts and Terminology
◦ Framework for Artificial Intelligence Systems Using Machine Learning
Jan 2018 China published “Artificial Intelligence Standardization White Paper.”
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31. http://unbias.wp.horizon.ac.uk/
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33. Responsible Research and Innovation
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